Please use this forum for questions or send an email to cplint@googlegroups.com.

The cplint on SWISH source is available from Github. It requires SWI-Prolog 7 installed from the latest GIT. We also provide a Docker image.

The cplint source is available from Github.

The source for using R in cplint was developed by Franco Masotti and is available from source is available from Github.

cplint on SWISH is described in:

• Fabrizio Riguzzi. Foundations of Probabilistic Logic Programming, River Publishers, Gistrup, Denmark, 2018.
• Marco Alberti, Elena Bellodi, Giuseppe Cota, Fabrizio Riguzzi, and Riccardo Zese. cplint on SWISH: Probabilistic logical inference with a web browser. Intelligenza Artificiale, 11(1):47-64, © IOS Press, 2017. [ bib | DOI | .pdf ]
• Fabrizio Riguzzi, Elena Bellodi, Evelina Lamma, Riccardo Zese, and Giuseppe Cota. Probabilistic logic programming on the web. Software: Practice and Experience, © Wiley, 2015. [ bib | DOI | .pdf ]
• Fabrizio Riguzzi, Riccardo Zese, and Giuseppe Cota. Probabilistic inductive logic programming on the web. In 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016; Bologna; Italy; 19 November 2016 through 23 November 2016, volume 10180 of Lecture Notes in Computer Science, pages 172-175. Springer, Cham, © Springer International Publishing AG, 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-58694-6_25. [ bib | DOI | .pdf ]

The algorithm for exact probabilistic inference (PITA) is described in:

• Fabrizio Riguzzi and Terrance Swift. Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics. Theory and Practice of Logic Programming, 13(Special Issue 02 - 25th Annual GULP Conference):279-302, © Cambridge University Press, March 2013. [ bib | DOI | .pdf ]
• Fabrizio Riguzzi and Terrance Swift. The PITA system: Tabling and answer subsumption for reasoning under uncertainty. Theory and Practice of Logic Programming, 27th International Conference on Logic Programming (ICLP'11) Special Issue, Lexington, Kentucky 6-10 July 2011, 11(4-5):433-449, © Cambridge University Press, 2011. [ bib | DOI | .pdf ]
• Fabrizio Riguzzi and Terrance Swift. Tabling and Answer Subsumption for Reasoning on Logic Programs with Annotated Disjunctions. In M. Hermenegildo and T. Schaub, editors, Technical Communications of the 26th Int'l. Conference on Logic Programming (ICLP'10), volume 7 of Leibniz International Proceedings in Informatics (LIPIcs), pages 162-171, Dagstuhl, Germany, July 2010. License Creative Commons Attribution-Noncommercial-No Derivative Works 3.0, Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik. [ bib | DOI | http ]

The algorithm for Monte Carlo inference (MCINTYRE) is described in:

• Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. Fundamenta Informaticae, 124(4):521-541, © IOS Press, 2013. [ bib | DOI | .pdf ]

The algorithm for Metropolis/Hastings sampling is described in:

• Arun Nampally and C. R. Ramakrishnan. Adaptive MCMC-Based Inference in Probabilistic Logic Programs. arXiv preprint arXiv:1403.6036, 2014. [ .pdf ]

The algorithm for parameter learning (EMBLEM) is described in:

• Elena Bellodi and Fabrizio Riguzzi. Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intelligent Data Analysis, 17(2):343-363, © IOS Press, 2013. [ bib | DOI | http | .pdf ]
• Elena Bellodi and Fabrizio Riguzzi. Experimentation of an expectation maximization algorithm for probabilistic logic programs. Intelligenza Artificiale, 8(1):3-18, © IOS Press, 2012. [ bib | DOI | .pdf ]

The SLIPCOVER algorithm for structure learning is described in:

• Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming, 15(2):169-212, © Cambridge University Press, 2015. [ bib | DOI | http | http ]

The LEMUR algorithm for structure learning is described in:

• Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-based Monte-Carlo structure learning of probabilistic logic programs. Machine Learning, 100(1):127-156, © Springer International Publishing, July 2015. [ bib | DOI | pdf ]

SWISH was originally written by Torbjörn Lager as a homage to SWI-Prolog. Jan Wielemaker designed and implemented the present version. The current SWISH application targets primarily at collaborative exploration of data. SWISH can be combined with e.g., CQL to explore relational (SQL) databases or sparkle to explore linked data. A ClioPatria plugin adds Prolog based exploration of RDF data to ClioPatria.

SWISH is a great tool for teaching Prolog. We provide a prototype of Learn Prolog Now! where SWISH is embedded to run examples and solve excercises from within your browser. Peter Flach prepared his book Simply Logical for SWISH.

Avatar graphics created by Noble Master Games, designed by Mei-Li Nieuwland.

The SWISH source is available from Github.